Reference points to determine the status of mostimportant commercially exploited stocks of thesmall-scale fisheries in three MacaronesianArchipelagos
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Lorena Couce-Montero
[1]
;
Airam-Guerra Marrero
[1]
;
David-Jiménez Alvarado
[1]
;
Ana Espino-Ruano
[1]
;
Ángelo Santana del Pino
[1]
;
José J. Castro-Hernández
[1]
-
[1] Universidad de Las Palmas de Gran Canaria
Universidad de Las Palmas de Gran Canaria
Gran Canaria, España
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- Localización: International Symposium on Artisanal and Recreational Fishing in Islands Systems (ISARFIS 2022) / coord. por María Esther Torres Padrón, 2023, ISBN 978-84-9042-479-7, págs. 25-26
- Idioma: inglés
- Enlaces
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Resumen
Fish stocks should be maintained at levels that can produce the maximumsustainable yield (MSY); however, for many stocks, the available biological and fisheriesinformation is not enough for such estimations. Although catch statistics are the most widelyaccessible fisheries data, very few stocks have reliable biomass assessments. Additionally,standardized data are not available for many fisheries, which prevents using catch per uniteffort (CPUE) as an actual indicator of changes in abundance. This makes extremely difficultthe estimation of strong biological and exploitation limits. Here, we describe a method forstock assessment of data-poor fisheries, including thirty-one representative species of theMacaronesian region, either because of the volume of their catches or because of theirbiological importance. The results obtained allow a preliminary evaluation of the status ofthese stocks based on the F/FMSY and B/BMSY criteria, which refer to the ratio of actualfishing mortality (F) to the level that would provide maximum sustainable yield (FMSY) andthe ratio of observed biomass (B) to the biomass that would provide maximum sustainableyield (BMSY), respectively. The results suggest that almost all the stocks assessed areoverexploited. However, they should be interpreted in the context of each Macaronesianarchipelago in combination with other indicators wherever possible. This is because themodel has limitations and uncertainties, and the robustness of the results therefore dependson the input data.
